Sampling and sampling methods
Sampling and sampling methods
Judgment sampling is a common nonprobability method. In this case, the batch is the population. However, this has the drawback of variable sample size, and different portions of the population may still be over- or under-represented due to chance variation in selections. As long as the starting point is randomized , systematic sampling is a type of probability sampling. Cluster Sampling: Cluster sampling is a method where the researchers divide the entire population into sections or clusters that represent a population. Stratified sampling In this method, the population is first divided into subgroups or strata who all share a similar characteristic. We visit each household in that street, identify all adults living there, and randomly select one adult from each household. Exploratory research: This sampling technique is widely used when researchers aim at conducting qualitative research, pilot studies or exploratory research. These selection parameters allow every member to have the equal opportunities to be a part of various samples.
This ensures that the statistical conclusions will be valid. If a sample is to be used, by whatever method it is chosen, it is important that the individuals selected are representative of the whole population.
Probability Sampling Methods 1. With stratified sampling, the population is divided into groups, based on some characteristic.
Non-probability samples. Consider this example. Hence, because the selection of elements is nonrandom, nonprobability sampling does not allow the estimation of sampling errors.
What is sampling
Thus, all possible samples of size did not have an equal chance of being selected; so this cannot be a simple random sample. As long as the starting point is randomized , systematic sampling is a type of probability sampling. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results. As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample. It is not 'simple random sampling' because different subsets of the same size have different selection probabilities — e. A population can be defined as including all people or items with the characteristic one wishes to understand. Example: We want to estimate the total income of adults living in a given street. Marketers can analyze which income groups to target and which ones to eliminate in order to create a roadmap that would definitely bear fruitful results. Systematic sampling is frequently used to select a specified number of records from a computer file. Main article: Sampling frame In the most straightforward case, such as the sampling of a batch of material from production acceptance sampling by lots , it would be most desirable to identify and measure every single item in the population and to include any one of them in our sample. B Yes, because each buyer in the sample had an equal chance of being sampled. The results usually must be adjusted to correct for the oversampling. This is usually and extension of convenience sampling. For example, Joseph Jagger studied the behaviour of roulette wheels at a casino in Monte Carlo , and used this to identify a biased wheel.
In such cases, using the snowball theory, researchers can track a few of that particular category to interview and results will be derived on that basis. Finally, since each stratum is treated as an independent population, different sampling approaches can be applied to different strata, potentially enabling researchers to use the approach best suited or most cost-effective for each identified subgroup within the population.
However, by selecting friends and acquaintances of subjects already investigated, there is a significant risk of selection bias choosing a large number of people with similar characteristics or views to the initial individual identified.
For example, when carrying out a survey of risk behaviours amongst intravenous drug users, participants may be asked to nominate other users to be interviewed. Consider the following example.
In nonprobability sampling, the degree to which the sample differs from the population remains unknown.
Sampling techniques ppt
Nonprobability sampling methods include convenience sampling , quota sampling and purposive sampling. This non-probability sampling method is used when there are time and cost limitations in collecting feedback. Another option is probability proportional to size 'PPS' sampling, in which the selection probability for each element is set to be proportional to its size measure, up to a maximum of 1. In other cases, the examined 'population' may be even less tangible. The results usually must be adjusted to correct for the oversampling. The numbers are placed in a bowl and thoroughly mixed. Each of the N population members is assigned a unique number. PPS sampling is commonly used for surveys of businesses, where element size varies greatly and auxiliary information is often available—for instance, a survey attempting to measure the number of guest-nights spent in hotels might use each hotel's number of rooms as an auxiliary variable. The researcher first identifies the relevant stratums and their actual representation in the population.
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